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library(velocyto.R)
载入需要的程辑包:Matrix
library(SeuratWrappers)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
library(Seurat)

载入程辑包:‘Seurat’

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    ALRAChooseKPlot, ExportToCellbrowser, ReadAlevin, RunALRA, StopCellbrowser
source("tianfengRwrappers.R")
载入需要的程辑包:dplyr

载入程辑包:‘dplyr’

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    filter, lag

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    intersect, setdiff, setequal, union

载入需要的程辑包:reticulate
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    expand, pack, unpack


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    DimPlot, DoHeatmap, LabelClusters, RidgePlot, VlnPlot


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    get_legend

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    smiths

NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
Registering fonts with R

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    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply,
    parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB

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    combine, intersect, setdiff, union

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    IQR, mad, sd, var, xtabs

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    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match,
    mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames,
    sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

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载入程辑包:‘SummarizedExperiment’

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    Assays
#首先需要取并集,然后使用已有的umap,在其上绘制速率图
velodata1 <- ReadVelocity(file ="./velo_data/CARconHEL.loom")
reading loom file via hdf5r...
velodata2 <- ReadVelocity(file ="./velo_data/CARconHEA0620.loom")
reading loom file via hdf5r...
velodata3 <- ReadVelocity(file ="./velo_data/CARconHEA0717.loom")
reading loom file via hdf5r...
#PA
# velodata1 <- ReadVelocity(file ="./velo_data/CARconDIS.loom")
# velodata2 <- ReadVelocity(file ="./velo_data/CARconDIS0620.loom")
# velodata3 <- ReadVelocity(file ="./velo_data/CARconDIS0717.loom")
#匹配两次的barcode
func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"1",sep = "-")
  return(s)
}

velodata1[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["spliced"]]@Dimnames[[2]],func))
velodata1[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["unspliced"]]@Dimnames[[2]],func))
velodata1[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata1[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"3",sep = "-")
  return(s)
}

velodata2[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["spliced"]]@Dimnames[[2]],func))
velodata2[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["unspliced"]]@Dimnames[[2]],func))
velodata2[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata2[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"5",sep = "-")
  return(s)
}

velodata3[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["spliced"]]@Dimnames[[2]],func))
velodata3[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["unspliced"]]@Dimnames[[2]],func))
velodata3[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata3[["ambiguous"]]@Dimnames[[2]],func))
# CA_dataset2 <- readRDS("CA_dataset2.rds")
ds2_AC <- readRDS("ds2_AC.rds")
sp1_velo <- as.Seurat(x = velodata1)

  |                                                                                                               
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  |                                                                                                               
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  |=========================================================================================================| 100%
sp2_velo <- as.Seurat(x = velodata2)

  |                                                                                                               
  |                                                                                                         |   0%Warning: Non-unique features (rownames) present in the input matrix, making unique

  |                                                                                                               
  |===================================                                                                      |  33%
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  |======================================================================                                   |  67%
  |                                                                                                               
  |=========================================================================================================| 100%
sp3_velo <- as.Seurat(x = velodata3)

  |                                                                                                               
  |                                                                                                         |   0%Warning: Non-unique features (rownames) present in the input matrix, making unique

  |                                                                                                               
  |===================================                                                                      |  33%
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  |=========================================================================================================| 100%
merge_velo <- merge(sp1_velo, c(sp2_velo,sp3_velo))
merge_velo <- subset(merge_velo, cells = WhichCells(ds2_AC))
rm(sp1_velo)
rm(sp2_velo)
rm(sp3_velo)
rm(velodata1)
rm(velodata2)
rm(velodata3)

merge_velo <- readRDS("dataset2_PA_velo.RDS")
Loading required package: Seurat
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
# saveRDS(merge_velo,"dataset2_AC_velo.RDS")
# merge_velo <- readRDS("dataset2_AC_velo.RDS")
ident.colors <- colors_list[1:length(x = levels(x = merge_velo))]
names(x = ident.colors) <- levels(x = merge_velo)
cell.colors <- ident.colors[Idents(object = merge_velo)]

names(x = cell.colors) <- colnames(x = merge_velo)
png("velocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = merge_velo, reduction = "umap"), vel = Tool(object = merge_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
delta projections ... sqrt knn ... transition probs ... done
calculating arrows ... done
grid estimates ... grid.sd= 0.5051766  min.arrow.size= 0.01010353  max.grid.arrow.length= 0.04539128  done
dev.off()
null device 
          1 
# plot <- tSNE.velocity.plot(vel = Tool(object = sp1_velo, slot = "RunVelocity"))
source("../vascular-analysis/seuratToAnnDataCombined.R")
h5ad_output <- "AC_SMC_velo.h5ad"  

library(reticulate)
library(Matrix)
writeMM(t(merge_velo@assays$RNA@counts), file='combined.mtx')
NULL
writeMM(t(merge_velo@assays$spliced@counts), file='spliced.mtx')
NULL
writeMM(t(merge_velo@assays$unspliced@counts), file='unspliced.mtx')
NULL
write.csv(rownames(merge_velo@assays$spliced@counts), file = "genes.csv", row.names = FALSE)
write.csv(merge_velo@reductions$umap@cell.embeddings, file = "umap.csv", row.names = FALSE)
write.csv(merge_velo@reductions$pca@cell.embeddings, file = "pca.csv", row.names = FALSE)

write.csv(colnames(merge_velo@assays$spliced@counts), file = "cells.csv", row.names = FALSE)
write.csv(merge_velo@meta.data, file = "meta.csv", row.names = FALSE)
source_python('~/scRNAseq/vascular-analysis/build.py')
build(h5ad_output, pca = TRUE, umap = TRUE)
file.remove('combined.mtx')
[1] TRUE
file.remove('spliced.mtx')
[1] TRUE
file.remove('unspliced.mtx')
[1] TRUE
file.remove('genes.csv')
[1] TRUE
file.remove('cells.csv')
[1] TRUE
file.remove('umap.csv')
[1] TRUE
file.remove('pca.csv')
[1] TRUE
file.remove('meta.csv')
[1] TRUE

只在SMC亚群中进行

SMC_velo <- subset(merge_velo, idents = "SMC")
RunVelocity(object = SMC_velo, deltaT = 1, kCells = 25, fit.quantile = 0.02)
ident.colors <- colors_list[1:length(x = levels(x = SMC_velo))]
names(x = ident.colors) <- levels(x = SMC_velo)
cell.colors <- ident.colors[Idents(object = SMC_velo)]

names(x = cell.colors) <- colnames(x = SMC_velo)
png("SMC_velocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = SMC_velo, reduction = "umap"), vel = Tool(object = SMC_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
dev.off()

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---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
library(velocyto.R)
library(SeuratWrappers)
library(Seurat)
source("tianfengRwrappers.R")

```

```{r}
#首先需要取并集，然后使用已有的umap，在其上绘制速率图
velodata1 <- ReadVelocity(file ="./velo_data/CARconHEL.loom")
velodata2 <- ReadVelocity(file ="./velo_data/CARconHEA0620.loom")
velodata3 <- ReadVelocity(file ="./velo_data/CARconHEA0717.loom")

#PA
# velodata1 <- ReadVelocity(file ="./velo_data/CARconDIS.loom")
# velodata2 <- ReadVelocity(file ="./velo_data/CARconDIS0620.loom")
# velodata3 <- ReadVelocity(file ="./velo_data/CARconDIS0717.loom")
```


```{r}
#匹配两次的barcode
func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"1",sep = "-")
  return(s)
}

velodata1[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["spliced"]]@Dimnames[[2]],func))
velodata1[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["unspliced"]]@Dimnames[[2]],func))
velodata1[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata1[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"3",sep = "-")
  return(s)
}

velodata2[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["spliced"]]@Dimnames[[2]],func))
velodata2[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["unspliced"]]@Dimnames[[2]],func))
velodata2[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata2[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"5",sep = "-")
  return(s)
}

velodata3[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["spliced"]]@Dimnames[[2]],func))
velodata3[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["unspliced"]]@Dimnames[[2]],func))
velodata3[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata3[["ambiguous"]]@Dimnames[[2]],func))
```


```{r}
# CA_dataset2 <- readRDS("CA_dataset2.rds")
ds2_AC <- readRDS("ds2_AC.rds")
sp1_velo <- as.Seurat(x = velodata1)
sp2_velo <- as.Seurat(x = velodata2)
sp3_velo <- as.Seurat(x = velodata3)
merge_velo <- merge(sp1_velo, c(sp2_velo,sp3_velo))
merge_velo <- subset(merge_velo, cells = WhichCells(ds2_AC))
rm(sp1_velo)
rm(sp2_velo)
rm(sp3_velo)
rm(velodata1)
rm(velodata2)
rm(velodata3)
```


```{r}
merge_velo <- merge_velo %>% 
    PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F,assay = "spliced") %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

VlnPlot(merge_velo,"percent.mt",group.by = "orig.ident")
VlnPlot(merge_velo, features = c("nFeature_spliced", "nCount_spliced"), ncol = 2,group.by = "orig.ident")

DimPlot(merge_velo, reduction = "umap",pt.size = 0.5,label = T)

merge_velo@reductions[["umap"]] <- ds2_AC@reductions[["umap"]]
merge_velo@reductions[["umap"]]@cell.embeddings <-  ds2_AC@reductions[["umap"]]@cell.embeddings[colnames(merge_velo),]

DimPlot(merge_velo, reduction = "umap",pt.size = 0.5,label = T)
merge_velo$Classification1 <- Idents(ds2_AC)
Idents(merge_velo) <- merge_velo$Classification1
merge_velo@assays[["RNA"]] <- ds2_AC@assays[["RNA"]]

f("CNN1",merge_velo)
f("CNN1",CA_dataset2)

```


```{r fig.width=6,fig.height=6}
# merge_velo <- FindClusters(merge_velo, resolution = 0.1)
umapplot(merge_velo)
multi_featureplot(c("LYZ","PTPRC","CD69","EPCAM","CDH1","PDGFRB","COL1A2","PECAM1","CLDN5"),merge_velo)

# CPA3 Mast_cell
## AC
# merge_velo <- RenameIdents(merge_velo,
#                          '0' = 'T_cell','1' = 'EC','2'='SMC1',
#                          '3'= 'Mod_SMC','4' ='Macrophage','5'='B_cell',
#                          '6'='SMC2','7'='Mast_cell')

## PA
merge_velo <- RenameIdents(merge_velo,
                         '0' = 'Macrophage','1' = 'T_cell1','2'='SMC1',
                         '3'= 'T_cell2','4' ='EC','5'='B_cell1',
                         '6'='Mast_cell','7'='T_cell3','8'='B_cell2')
merge_velo$'Classification1' <- Idents(merge_velo)
merge_velo@assays[["RNA"]] <- CA_dataset2@assays[["RNA"]]

saveRDS(merge_velo,"dataset2_PA_velo.RDS")
```


```{r}
merge_velo <- readRDS("dataset2_PA_velo.RDS")
merge_velo <- RunVelocity(object = merge_velo, deltaT = 1, kCells = 25, fit.quantile = 0.02, ncores = 3) # fitting gamma coefficients 用时长
```


```{r}
# saveRDS(merge_velo,"dataset2_AC_velo.RDS")
# merge_velo <- readRDS("dataset2_AC_velo.RDS")
ident.colors <- colors_list[1:length(x = levels(x = merge_velo))]
names(x = ident.colors) <- levels(x = merge_velo)
cell.colors <- ident.colors[Idents(object = merge_velo)]

names(x = cell.colors) <- colnames(x = merge_velo)
png("PAvelocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = merge_velo, reduction = "umap"), vel = Tool(object = merge_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
dev.off()

# plot <- tSNE.velocity.plot(vel = Tool(object = sp1_velo, slot = "RunVelocity"))
```


```{r}
source("../vascular-analysis/seuratToAnnDataCombined.R")
```

```{r}
h5ad_output <- "AC_SMC_velo.h5ad"  

library(reticulate)
library(Matrix)
writeMM(t(merge_velo@assays$RNA@counts), file='combined.mtx')
writeMM(t(merge_velo@assays$spliced@counts), file='spliced.mtx')
writeMM(t(merge_velo@assays$unspliced@counts), file='unspliced.mtx')
write.csv(rownames(merge_velo@assays$spliced@counts), file = "genes.csv", row.names = FALSE)
write.csv(merge_velo@reductions$umap@cell.embeddings, file = "umap.csv", row.names = FALSE)
write.csv(merge_velo@reductions$pca@cell.embeddings, file = "pca.csv", row.names = FALSE)

write.csv(colnames(merge_velo@assays$spliced@counts), file = "cells.csv", row.names = FALSE)
write.csv(merge_velo@meta.data, file = "meta.csv", row.names = FALSE)
source_python('~/scRNAseq/vascular-analysis/build.py')
build(h5ad_output, pca = TRUE, umap = TRUE)
file.remove('combined.mtx')
file.remove('spliced.mtx')
file.remove('unspliced.mtx')
file.remove('genes.csv')
file.remove('cells.csv')
file.remove('umap.csv')

file.remove('pca.csv')
file.remove('meta.csv')


```


# 只在SMC亚群中进行
```{r}
SMC_velo <- subset(merge_velo, idents = "SMC")
RunVelocity(object = SMC_velo, deltaT = 1, kCells = 25, fit.quantile = 0.02)
ident.colors <- colors_list[1:length(x = levels(x = SMC_velo))]
names(x = ident.colors) <- levels(x = SMC_velo)
cell.colors <- ident.colors[Idents(object = SMC_velo)]

names(x = cell.colors) <- colnames(x = SMC_velo)
png("SMC_velocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = SMC_velo, reduction = "umap"), vel = Tool(object = SMC_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
dev.off()
```


Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

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The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
